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Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

Jiyong Oh, Syed M. Raza, Lusungu J. Mwasinga, Moonseong Kim, Hyunseung Choo

TL;DR

The paper addresses autonomous UAV path planning under connectivity constraints in dense urban environments. It introduces DUPAC, a PPO-based DRL framework that learns continuous-action UAV trajectories and opportunistic GBS handovers guided by RSRP rewards to maintain connectivity. Key contributions include integrating connectivity assurance into path planning, transforming the action space from discrete to continuous, and evaluating in a realistic 3D Unity urban model with 30 GBSs and 3GPP-style path-loss. Empirical results show DUPAC achieves approximately 7.5% higher average RSRP with only ~2% extra flight distance, and extends periods of excellent connectivity by about 9-11% depending on distance, demonstrating practical benefits for mission-critical UAV operations.

Abstract

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

TL;DR

The paper addresses autonomous UAV path planning under connectivity constraints in dense urban environments. It introduces DUPAC, a PPO-based DRL framework that learns continuous-action UAV trajectories and opportunistic GBS handovers guided by RSRP rewards to maintain connectivity. Key contributions include integrating connectivity assurance into path planning, transforming the action space from discrete to continuous, and evaluating in a realistic 3D Unity urban model with 30 GBSs and 3GPP-style path-loss. Empirical results show DUPAC achieves approximately 7.5% higher average RSRP with only ~2% extra flight distance, and extends periods of excellent connectivity by about 9-11% depending on distance, demonstrating practical benefits for mission-critical UAV operations.

Abstract

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.
Paper Structure (7 sections, 8 equations, 4 figures)

This paper contains 7 sections, 8 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. DRL-Driven UAV Path Planning with Assured Connectivity (DUPAC)
  4. Performance Evaluation
  5. Experimental setup
  6. Results
  7. Conclusion and Future Directions

Figures (4)

  • Figure 1: Proximal Policy Optimization (PPO) framework for the proposed UAV Path Planning with Assured Connectivity.
  • Figure 2: Snapshot of the simulated 3D dense urban environment of New York City, USA, and RSRP coverage heatmap for multiple altitudes caused by 30 base Stations.
  • Figure 3: DUPAC impact on UAV flight distance and RSRP trade-off compared to base method.
  • Figure 4: DUPAC effect on RSRP distribution during UAV flight compared to base method.